[Liste-proml] Eleventh Madrid UPM Advanced Statistics and Data Mining Summer School (June 27th - July 8th, 2016) - Early registration reminder

asdm at fi.upm.es asdm at fi.upm.es
Mar 5 Avr 13:49:17 CEST 2016


Dear colleagues,

We would like to remind you that early registration for the Madrid UPM Advanced Statistics and Data Mining summer school is open until June 6th. The summer school will be held in Boadilla del Monte, near Madrid, from June 27th to July 8th. This year's edition comprises 12 week-long courses (15 lecture hours each), given during two weeks (six courses each week). Attendees may register in each course independently. No restrictions, besides those imposed by timetables, apply on the number or choice of courses.

For the second time in a row, INOMICS has selected our summer school as one of the world's top ten summer schools in mathematics and statistics. You can read more at http://bit.ly/1TweJjj

Early registration is *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website:

http://www.dia.fi.upm.es/ASDM

There is a 25% discount for members of Spanish AEPIA and SEIO societies.  

Please, forward this information to your colleagues, students, and whoever you think may find it interesting.

Best regards,

Pedro Larranaga, Concha Bielza, Bojan Mihaljevic and Laura Anton-Sanchez.
-- School coordinators.

*** List of courses and brief description ***

* Week 1 (June 27th - July 1st, 2016) *

1st session: 9:45-12:45
Course 1: Bayesian Networks (15 h)
      Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Practical demonstration: GeNIe, Weka, Bayesia, R.

Course 2: Time Series(15 h)
      Basic concepts in time series. Descriptive methods for time series. Linear models for time series. Extensions. Practical demonstration: R.

2nd session: 13:45-16:45
Course 3: Supervised Pattern Recognition (15 h)
      Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Practical demonstration: Weka. 

Course 4: Bayesian Inference (15 h)
      Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. Practical demonstration: R and WinBugs.

3rd session: 17:00 - 20:00
Course 5: Neural Networks and Deep Learning (15 h)
      Introduction. Training algorithms. Learning and Optimization. MLPs in practice. Deep Networks. Practical session: Python with keras and Jupyter notebooks.

Course 6: Unsupervised Pattern Recognition (15 h)
      Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advise. Practical session: R.


* Week 2 (July 4th - July 8th, 2016) *

1st session: 9:45-12:45
Course 7: Statistical Inference (15 h)
      Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Practical demonstration: R.

Course 8: Big Data with Apache Spark (15 h)
      Introduction. Spark framework and APIs. Data processing with Spark. Spark streaming. Machine learning with Spark MLlib. 

2nd session: 13:45-16:45
Course 9: Text Mining (15 h)
      Introduction. Language Modeling. Text Similarity. Text Classification. Information Extraction. Practical session: Python, with Jupyter notebooks.

Course 10: Feature Subset Selection (15 h)
      Introduction. Filter approaches. Embedded methods. Wrapper methods. Advanced topics. Practical session: R and Weka.      
      
3rd session: 17:00-20:00
Course 11: Support Vector Machines and Regularized Learning (15 h)
      Introduction. SVM models. SVM learning algorithms. Regularized learning. Convex optimization for regularized learning. Practical session: Python with scikit-learn, Jupyter notebooks.
      
Course 12: Hidden Markov Models (15 h)
      Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs. Practical session: HTK.

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